Visualization of GPS Residual Errors - Coursework Example

HOW TO VISUALIZE GPS RESIDUAL ERRORS By Student’s name Course code and name Professor’s name University name City, State Date of submission Abstract This document covers the data visualization process as part of research process in order to establish the best technique to present a Global Positioning System (GPS) residual data. The advantages attached to visual presentation of data cannot be avoided if researchers are to ensure that they propagate the coded message with ease. Introduction Data visualization has become a challenging phase of research considering the wide range of methods that are available for this purpose. Raw data if presented well may be of great help to a researcher especially when it comes to drawing conclusions and recommendations in the long run. The complexity that raw data poses to the to the audience calls for researchers to choose their mode of presentation painstakingly in order to avoid misconceptions. A good visualization method is characterized by; ease of message conveyance, ability to make the topic interesting, text/ visualization harmony, real life icons, color consistency, quick to understand and appropriateness in a given context. This essay looks into improvements that could have been carried out on the visualization methods of GPS error data collected during a “Research to identify error margins in a GPS system”. This data was presented in a tabular manner without consideration of other methods that could have aided in making it easy for depiction to end audience. The suggested improvements on this visualization method are the use of line graphs and histograms. Nature of Data GPS errors result from atmospheric effects, multipath effects, and ephemeris and clock errors. It is important to note that for GPS error data to be effective, by definition the geographical information must be portrayed. For the purpose of this essay, the information in includes but not limited to time, degree, minute, second, location, latitude, the original distance, the measured and GPS residual error. Time represents the period between which the measurements were taken. Degree, minute and second presents the three parameters of longitude or latitude coordinate presentation depending on level of accuracy. For this particular dataset, the required accuracy levels call for all the three parameters. Conversion of the above quantitative data parameters requires up to eight decimal places in order to give the correct residual difference between the original distance and the measured distance. The location means the compass direction of a point, that is; North, South, West and East. The following table presents location data collected for the sake of GPS error analysis. Degree Minute Second Location Longitude 144 8 24.6 W 144.14016667 144 8 24.48 W 144.14013333 144 8 24.48 W 144.14013333 144 8 24.54 W 144.14015000 Table 1: Sample Global Positioning System parameters data Time (second) Original Measured GPS error 0 9 12 -3 30 6 12 -6 60 9 12 -3 90 11 12 -1 Table 2: Sample GPS distance error calculations. The above data was collected by use of simple handheld GPS receiver in conjunction with a mobile phone on carefully selected points within one day. The threshold distance was determined by the course manual as greater than or equal to 10m. The collected data was entered into an excel sheet containing predetermined formulae for the calculation of residual GPS errors and presented in a report as an analysis. Target Audience This data would matter a lot within a team sphere, peers and eventually to the professor who is directly involved with this study module. However, at an advanced level, this data would matter to individuals who use GPS in bettering their activities for example marine seismic survey, cadastral survey, transit systems, vehicle navigation, precision engineering and mapping. This data possesses a high magnitude of importance in that it is used in the calibration exercise of GPS systems. While this data is meant for GPS correction, lack of accuracy in it may translate to accidents, loss of time and loss of direction to those who are solely dependent on these navigation systems. On the other hand, the accuracy and proper presentation of this data may be very productive for use in this industry. Typical Presentation of GPS Error Data The GPS error data or residual error data is normally presented in form of a table containing all the longitudinal, latitude and distance data collected in a field exercise. As shown in the above mode of data presentation (Table 1 and Table 2), only quantitative can be depicted since data transformation has not been carried out yet. It would also be difficult for untransformed data to be used by a targeted audience whether professional or layman. On the other hand, this data is rather seen as raw since it contains nothing else other than tables which are not self-explanatory or even proven according to audience expectations. Tables also require skills and a high aptitude in order to correctly read and decipher them. This needs a lot of clarification which makes the report even longer. The advancement in analysis techniques has over time come up with standards of determining or analyzing collected data. In case of this GPS residual data, analysis could have to be done for presentation in another suitable form. This could utilize tables of distribution, frequency charts, standard deviation charts, reference and investigative tables as alternative modes to raw data tables. However, visual data in form of graphs could be easily understood or even remembered by the audience hence a preferable method. Therefore, exploration on how a line graph and histogram could be utilized in presenting this data comes in handy. Visual Presentation of GPS Residual Data Data presentations in visual formats are encouraged since they offer a better understanding as compared to tables. In most cases, charts and graphs which are mainly used for this purpose highlight only the important data while leaving out the large amounts of numbers which do not offer any outstanding importance to the audience. In other words graphs only show vital patterns and trends that are visually attractive and smart in depicting the message. As much as this mode is not suitable for detailed data that is meant to be precise, the advantages outweigh the disadvantages. It is also important to remember that the comparison and analysis purpose attached to this method is outstanding. For GPS residual data deviation, it is easy to forecast the trends and statistical changes in an attribute in a case where the graphical visualization method is used. Examples of graphical display features that can be used to depict GPS residual data are shown in figure 1 and 2 below. Figure 1: A line graph of error against time. Figure 2: A histogram showing spread of errors A line graph is mainly dedicated to time series rather known as trends. These may contain multiple lines or single lines depending on the comparative parameters being investigated. This visualization technique makes it easier for the audience’s eyes to follow along the trend thereby propagating a clearer picture of what is expected (Cvetkovic et al., 2004). A histogram is meant to depict the distribution of a given attribute or parameter especially for a large dataset. The generalization of data makes it easier for the classification concept to be applied. It is also easy to calculate histograms due to the increased availability of dedicated data visualization programs such as R. Improved results are depicted in this report since the Gaussian curve shape is easy to ready – histograms range from the lowest value to the highest value. Another reason as to why this could be the best method is that, it is involves minimal parameters within its boundaries (Vining & Kowalski, 2011). Histogram and line graph methods of displaying data conclude lot in comparison to the tabular method of raw data presentation. The summation of an enormous list of data into a simple graph is particularly appalling considering what the amount of time the audience may incur in coming up with investigative conclusion for Table 1 and 2. This is made easier through simple methods of data depiction which even lasts in the mind of the audience. The highest and the lowest residual errors can easily be noted from the line graph while the Gaussian spread is shown comfortably on the histogram on Figure 2. Conclusion Data visualization is an important process in data analysis for various types of audiences. This essay applies a case of a “Research to identify error margins in a GPS system” in order to establish how important a line graph and a histogram may be important in visualizing research data collected during this investigation. It is clear that as much as data presented in graphical form is attractive, it also gives a summary of the data collected thereby increasing audience understanding and reducing the time used on analysis especially by laymen. List of References Cvetkovic, D., Rowlinson, P. & imic, S., 2004. Spectral Generalizations of Line Graphs: On Graphs with Least Eigenvalue -2. Cambridge: Cambridge University Press. Vining, G.G. & Kowalski, S.M., 2011. Statistical Methods for Engineers. Massachusetts: Cengage Learning. Read More